Decision Trees
Every other model in this section requires some statistical literacy to interpret. A decision tree doesn't — you can hand the diagram to someone with no ML background and they can trace a prediction themselves, one yes/no question at a time. That transparency comes at a real cost: trees are greedy, and greedy is not the same as optimal.
Random Forests and Bagging
A single unconstrained decision tree overfits badly. Grow a few hundred of them, each on a slightly different random sample of the data, and average their predictions — and the overfitting largely cancels out. That's the entire idea behind bagging, and random forests are bagging applied specifically to trees with one extra trick.